Skip to content
Virtual event

Building public confidence in data-driven systems

Findings of the Office for Statistics Regulation review into the 2020 exam results algorithm, and why public confidence in data-driven systems matters

Very good exam results. Pencil in shot.
Date and time
2:00pm – 3:00pm, 13 May 2021 (BST)

The Ofqual A-level exam results algorithm prompted wide societal debate about the conditions that would engender public trust and confidence in data-driven systems.

In this event, the Office for Statistics Regulation share findings from their UK-wide review of the exam models deployed in 2020, focusing on the importance of confidence in models, specific factors that impacted on confidence in the A-level algorithm, and drawing on lessons for the future.

The Ada Lovelace Institute share insights from a diverse profile of public engagement and deliberation work undertaken during 2020, on the conditions that engender public confidence in data and related technologies, at times of public health emergency and beyond.

Watch the event back here:

This video is embedded with YouTube’s ‘privacy-enhanced mode’ enabled although it is still possible that if you play this video it may add cookies. Read our Privacy policy and Digital best practice for more on how we use digital tools and data.


  • Andrew Strait

    Associate Director (Emerging technology & industry practice)


  • Emily Carless

    Children, Education and Skills lead regulator, Office for Statistics Regulation
  • Reema Patel

    Associate Director (Engagement), Ada Lovelace Institute


  • Ed Humpherson

    Director General for Regulation, Office for Statistics Regulation
  • Michael Hodge

    Head of Data and Automation, Office for Statistics Regulation

Over the course of the hour we explore some of the following questions:

  • What measures can be taken to build public confidence in the design, deployment and use of data-driven systems?
  • Why does public confidence in data-driven systems matter?
  • What is the role of co-design, inclusion and participation in ensuring the responsible and effective stewardship of data?

Related content